NUMTS

###Before ocntinuing shall check to add the int(CHR) from the python script…. and rerun all the samples.

###Libraries

rm(list = ls(all.names = TRUE))
#.libPaths("/data/botos/RLibs/")
#.libPaths("/data/botos/RLibs/")
#BiocManager::install("S4Vectors",update = TRUE,ask = FALSE,force = TRUE)
#install.packages("wesanderson",lib = .libPaths()[1])
library(wesanderson)
library(tidyr)
library(tidyverse)

── Attaching core tidyverse packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.0     ✔ purrr     1.0.1
✔ forcats   1.0.0     ✔ readr     2.1.4
✔ ggplot2   3.4.1     ✔ stringr   1.5.0
✔ lubridate 1.9.2     ✔ tibble    3.2.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(brew)
library(circlize)

========================================
circlize version 0.4.15
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

library(S4Vectors)

Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:lubridate’:

    intersect, setdiff, union

The following objects are masked from ‘package:dplyr’:

    combine, intersect, setdiff, union

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted,
    lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min


Attaching package: ‘S4Vectors’

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    second, second<-

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    first, rename

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    expand

The following objects are masked from ‘package:base’:

    expand.grid, I, unname

library(GenomicRanges)

Loading required package: IRanges

Attaching package: ‘IRanges’

The following object is masked from ‘package:lubridate’:

    %within%

The following objects are masked from ‘package:dplyr’:

    collapse, desc, slice

The following object is masked from ‘package:purrr’:

    reduce

Loading required package: GenomeInfoDb

# unloadNamespace("IRanges")
# unloadNamespace("GenomeInfoDb")
# unloadNamespace("rtracklayer")
# unloadNamespace("plyranges")
library(IRanges)
library(karyoploteR)

Loading required package: regioneR
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Read files

##Load the data
files <- list.files(all.files = TRUE,path = "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti/",
                    pattern = "*.mt.disc.sam.cluster.summary.tsv",
                    recursive = TRUE, 
                    full.names = TRUE)
#Sort the files by the number of output
files <- files[order(nchar(files))]
files

 [1] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_017_E20_F03/WGS_017_E20_F03.mt.disc.sam.cluster.summary.tsv"
 [2] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_021_E20_F02/WGS_021_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [3] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_024_E20_F02/WGS_024_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [4] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_025_E20_F02/WGS_025_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [5] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_038_E20_F07/WGS_038_E20_F07.mt.disc.sam.cluster.summary.tsv"
 [6] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_040_E20_F02/WGS_040_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [7] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_042_E20_F02/WGS_042_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [8] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_043_E20_F02/WGS_043_E20_F02.mt.disc.sam.cluster.summary.tsv"
 [9] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_047_E20_F02/WGS_047_E20_F02.mt.disc.sam.cluster.summary.tsv"
[10] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_050_E20_F02/WGS_050_E20_F02.mt.disc.sam.cluster.summary.tsv"
[11] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_050_E20_F03/WGS_050_E20_F03.mt.disc.sam.cluster.summary.tsv"
[12] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_060_E20_F06/WGS_060_E20_F06.mt.disc.sam.cluster.summary.tsv"
[13] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_063_E20_F02/WGS_063_E20_F02.mt.disc.sam.cluster.summary.tsv"
[14] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_080_E20_F02/WGS_080_E20_F02.mt.disc.sam.cluster.summary.tsv"
[15] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_084_E20_F02/WGS_084_E20_F02.mt.disc.sam.cluster.summary.tsv"
[16] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_088_E20_F05/WGS_088_E20_F05.mt.disc.sam.cluster.summary.tsv"
[17] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_088_E20_F06/WGS_088_E20_F06.mt.disc.sam.cluster.summary.tsv"
[18] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_095_E20_F11/WGS_095_E20_F11.mt.disc.sam.cluster.summary.tsv"
[19] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_115_E20_F03/WGS_115_E20_F03.mt.disc.sam.cluster.summary.tsv"
[20] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_132_E20_F10/WGS_132_E20_F10.mt.disc.sam.cluster.summary.tsv"
[21] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_134_E20_F02/WGS_134_E20_F02.mt.disc.sam.cluster.summary.tsv"
[22] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_211_E20_F02/WGS_211_E20_F02.mt.disc.sam.cluster.summary.tsv"
[23] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_220_E20_F07/WGS_220_E20_F07.mt.disc.sam.cluster.summary.tsv"
[24] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_230_E20_F06/WGS_230_E20_F06.mt.disc.sam.cluster.summary.tsv"
[25] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_240_E20_F05/WGS_240_E20_F05.mt.disc.sam.cluster.summary.tsv"
[26] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_246_E20_F05/WGS_246_E20_F05.mt.disc.sam.cluster.summary.tsv"
[27] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_246_E20_F07/WGS_246_E20_F07.mt.disc.sam.cluster.summary.tsv"
[28] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_249_E20_F02/WGS_249_E20_F02.mt.disc.sam.cluster.summary.tsv"
[29] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_252_E20_F02/WGS_252_E20_F02.mt.disc.sam.cluster.summary.tsv"
[30] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_253_E20_F01/WGS_253_E20_F01.mt.disc.sam.cluster.summary.tsv"
[31] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_262_E20_F07/WGS_262_E20_F07.mt.disc.sam.cluster.summary.tsv"
[32] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_266_E20_F04/WGS_266_E20_F04.mt.disc.sam.cluster.summary.tsv"
[33] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_289_E20_F02/WGS_289_E20_F02.mt.disc.sam.cluster.summary.tsv"
[34] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_299_E20_F09/WGS_299_E20_F09.mt.disc.sam.cluster.summary.tsv"
[35] "/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_WGS_300_E20_F05/WGS_300_E20_F05.mt.disc.sam.cluster.summary.tsv"

#files
#name_files <- gsub("/home/marius/Documents/Projects/prsa/02_Data/STAR/mapping/release102/","",files)
# name_files <- gsub("D:/PhD/Projects/prsa/02_Data/STAR/mapping/release102/","",files)
name_files <- gsub("/data/botos/2022_12_01_NUMTS_Aedes_Aegypti//NUMTS_OUTPUT_","",
                   gsub(".mt.disc.sam.cluster.summary.tsv","",
                       gsub("WGS.*F[0-9][0-9]*/","",files)))

names(files) <- name_files
#head(files)
#Read it inside lists.
#lapply(names(files),function(x) head(x))
dfs_numts <- lapply(files,function(fj) read.table(fj,header = FALSE))
paste0("List of ",length(dfs_numts)," samples.\n")

[1] "List of 35 samples.\n"

# Split the column into the position data
dfs_numts_pos <- lapply(dfs_numts,function(df) {
  separate(data = df,col = V3,into = c("chr","start","end","MT","mt_start","mt_end"),sep = "_")
})

# add the length of the segment in the chr or mt chromosomes
dfs_numts_pos_match_len <- lapply(dfs_numts_pos,function(df) {
  df <- dplyr::mutate(df,
                      chr_matchLen = as.integer(end) - as.integer(start),
                      mt_matchLen = as.integer(mt_end) - as.integer(mt_start))
  return(df)})

#print

dfs_numts_pos_match_len$WGS_017_E20_F03

Check names

# V5 = Cluster sequences that are in the same cluster 500bp apart gap.
# V6 = cluster of read sequences that are max 500bp apart and mininum 2
lapply(dfs_numts_pos_match_len,function(df) {
  df[["V2"]][1] 
  # names(df)
  # df %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::filter(chr_matchLen > 5 & mt_matchLen > 5) %>%  dplyr::arrange(V6)
  })

$WGS_017_E20_F03
[1] "WGS_017_E20_F03"

$WGS_021_E20_F02
[1] "WGS_021_E20_F02"

$WGS_024_E20_F02
[1] "WGS_024_E20_F02"

$WGS_025_E20_F02
[1] "WGS_025_E20_F02"

$WGS_038_E20_F07
[1] "WGS_038_E20_F07"

$WGS_040_E20_F02
[1] "WGS_040_E20_F02"

$WGS_042_E20_F02
[1] "WGS_042_E20_F02"

$WGS_043_E20_F02
[1] "WGS_043_E20_F02"

$WGS_047_E20_F02
[1] "WGS_047_E20_F02"

$WGS_050_E20_F02
[1] "WGS_050_E20_F02"

$WGS_050_E20_F03
[1] "WGS_050_E20_F03"

$WGS_060_E20_F06
[1] "WGS_060_E20_F06"

$WGS_063_E20_F02
[1] "WGS_063_E20_F02"

$WGS_080_E20_F02
[1] "WGS_080_E20_F02"

$WGS_084_E20_F02
[1] "WGS_084_E20_F02"

$WGS_088_E20_F05
[1] "WGS_088_E20_F05"

$WGS_088_E20_F06
[1] "WGS_088_E20_F06"

$WGS_095_E20_F11
[1] "WGS_095_E20_F11"

$WGS_115_E20_F03
[1] "WGS_115_E20_F03"

$WGS_132_E20_F10
[1] "WGS_132_E20_F10"

$WGS_134_E20_F02
[1] "WGS_134_E20_F02"

$WGS_211_E20_F02
[1] "WGS_211_E20_F02"

$WGS_220_E20_F07
[1] "WGS_220_E20_F07"

$WGS_230_E20_F06
[1] "WGS_230_E20_F06"

$WGS_240_E20_F05
[1] "WGS_240_E20_F05"

$WGS_246_E20_F05
[1] "WGS_246_E20_F05"

$WGS_246_E20_F07
[1] "WGS_246_E20_F07"

$WGS_249_E20_F02
[1] "WGS_249_E20_F02"

$WGS_252_E20_F02
[1] "WGS_252_E20_F02"

$WGS_253_E20_F01
[1] "WGS_253_E20_F01"

$WGS_262_E20_F07
[1] "WGS_262_E20_F07"

$WGS_266_E20_F04
[1] "WGS_266_E20_F04"

$WGS_289_E20_F02
[1] "WGS_289_E20_F02"

$WGS_299_E20_F09
[1] "WGS_299_E20_F09"

$WGS_300_E20_F05
[1] "WGS_300_E20_F05"

###Visualize the data in violins

library(ggplot2)
library(dplyr)
# Iterate over the list of dataframes
plots_df_numts <- lapply(dfs_numts_pos_match_len,function(df){
  # Create the plot
  fig <- ggplot(df  %>% dplyr::group_by(V2) %>% dplyr::filter(chr_matchLen > 5 & mt_matchLen > 5), aes(x=chr_matchLen,y="")) +
    geom_violin(fill="#FAF0E6", alpha=0.4) +
    geom_jitter(aes(color="Nuclear"), size=3, alpha=0.3,show.legend = FALSE) +
    #geom_boxplot(width=0.1) +
    scale_color_manual(values="#000080") +
    # xlim(-30,max(df$chr_matchLen)+30) +
    #xlim(0,1100) +
    scale_x_continuous(breaks = seq(0,1100,100),limits=c(-30,1100)) +
    labs(title=paste0("Nuclear: ",unique(df$V2))) +
    theme(axis.text.y=element_blank(),
          axis.ticks.y=element_blank(),
          axis.title.y = element_blank(),
          plot.background = element_rect(fill = "white"),
          panel.background = element_rect(fill = "white"),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  
  fig2 <- ggplot( df %>% dplyr::group_by(V2) %>% dplyr::filter(chr_matchLen > 0 & mt_matchLen > 0), aes(x=mt_matchLen,y="")) +
    geom_violin(fill="#FAF0E6", alpha=0.4) +
    geom_jitter(aes(color="Mitochondrial"), size=3, alpha=0.3,show.legend = FALSE) +
    #geom_boxplot(width=0.1) +
    scale_color_manual(values="#800020") +
    # xlim(-30,max(df$mt_matchLen)+30) +
    #xlim(0,12900) +
    scale_x_continuous(breaks = seq(0,12900,500),limits=c(-30,12900)) +
    labs(title=paste0("Mitochondrial: ",unique(df$V2))) +
    theme(axis.text.y=element_blank(),
          axis.ticks.y=element_blank(),
          axis.title.y = element_blank(),
          plot.background = element_rect(fill = "white"),
          panel.background = element_rect(fill = "white"),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  
  # Print the plot
  print(list(fig,fig2))
  
  # Add the plot to the list
  #plots <- c(plots, list(fig))
  return(plots=list(fig,fig2))
})

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par(mfrow = c(35, 1))#, mar = rep(0.5, 4))
library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:BiocGenerics':

    combine

The following object is masked from 'package:dplyr':

    combine

lapply(names(plots_df_numts),function(nm) {
  grid.arrange(grobs=plots_df_numts[[nm]],ncol=2)})

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

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TableGrob (1 x 2) "arrange": 2 grobs

NA

NA

Filter numts that are not 0 in the MT or the CHR and show how many numts per RIL. (inlcude maybe the clustering of the numts later)

lapply(dfs_numts_pos_match_len,function(df) {
  df %>% dplyr::group_by(V2) %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>% dplyr::summarise(n=n())})

$WGS_017_E20_F03

$WGS_021_E20_F02

$WGS_024_E20_F02

$WGS_025_E20_F02

$WGS_038_E20_F07

$WGS_040_E20_F02

$WGS_042_E20_F02

$WGS_043_E20_F02

$WGS_047_E20_F02

$WGS_050_E20_F02

$WGS_050_E20_F03

$WGS_060_E20_F06

$WGS_063_E20_F02

$WGS_080_E20_F02

$WGS_084_E20_F02

$WGS_088_E20_F05

$WGS_088_E20_F06

$WGS_095_E20_F11

$WGS_115_E20_F03

$WGS_132_E20_F10

$WGS_134_E20_F02

$WGS_211_E20_F02

$WGS_220_E20_F07

$WGS_230_E20_F06

$WGS_240_E20_F05

$WGS_246_E20_F05

$WGS_246_E20_F07

$WGS_249_E20_F02

$WGS_252_E20_F02

$WGS_253_E20_F01

$WGS_262_E20_F07

$WGS_266_E20_F04

$WGS_289_E20_F02

$WGS_299_E20_F09

$WGS_300_E20_F05

NA

List of Samples and number of numts per sample in the Chr 1,2,3

# counts_of_numts_longer_than_20bp <- lapply(dfs_numts_pos_match_len,function(df) {
#   df %>% dplyr::group_by(V2) %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>% dplyr::summarise(n=n())})
# 

counts_of_numts_longer_than_20bp <- lapply(dfs_numts_pos_match_len,function(df) df %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20)   %>% dplyr::mutate_at(c("mt_start","mt_end","mt_matchLen"),as.numeric) %>% group_by(group = cut(mt_matchLen, breaks = seq(0,17000,50))) %>% summarize(mt_matchLen = n(),sampleID = df[[2]][1]))

counts_of_numts_longer_than_20bp_df <- do.call(rbind,counts_of_numts_longer_than_20bp)

#dir.create("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table",recursive = TRUE)

write.table(counts_of_numts_longer_than_20bp_df,file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/numts_ranges_all_samples.csv",sep = ",")

p <- counts_of_numts_longer_than_20bp_df %>% ggplot(aes(x=as.factor(group), y=mt_matchLen,fill = ("red"))) +
  geom_col(alpha=0.7) +
  #geom_col(data= dataT[1:39,],mapping = aes(x=date,y=income/2 ,fill=d1)) +
  geom_line(data= counts_of_numts_longer_than_20bp_df,group=1,mapping=aes(x=as.factor(group),y=mt_matchLen)) +
  # geom_point(data = counts_of_numts_longer_than_20bp_df,
  #            aes(x = counts_of_numts_longer_than_20bp_df$group[which.max(counts_of_numts_longer_than_20bp_df$mt_matchLen)],
  #                y = counts_of_numts_longer_than_20bp_df$mt_matchLen[which.max(counts_of_numts_longer_than_20bp_df$mt_matchLen)]), color="black",size=3) +
  #geom_area(position = "identity", alpha = 0.5,color="red") +
  #geom_bar(stat = "identity") +
  #stat_density(aes(geom="line",position="identity")) + 
  #geom_density(aes(after_stat(count))) +
  #xlim(c(0,17000)) +
  #coord_flip() +
  labs(fill = "Numts Length Group") +
  scale_x_discrete("group") +
  facet_wrap(~sampleID,ncol = 2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) 

p

svg(filename = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/numts_ranges_all_samples_barplots_per_sample.svg",
    width = 16,
    height = 31)

p
dev.off()

png 
  2 

Genes for circos

###add mt genes
mt_genes_ae <- read.table(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/Visualization_samples/mt_ae_gtf_genes_trna_rrna.txt",sep = "\t")
 
# unique(mt_genes_ae$V5) 
# unique(mt_genes_ae$V6) 
# unique(mt_genes_ae$V7)
# unique(mt_genes_ae$V4) 

mt_genes_ae$paste <- paste(mt_genes_ae$V5,mt_genes_ae$V6,sep="_")
mt_genes_ae <- mt_genes_ae[,c("V2","V3","paste")]
mt_genes_ae <- mt_genes_ae %>% dplyr::filter(paste != "_") %>% dplyr::mutate(Genes= str_remove(paste,"^_|_$"))
mt_genes_ae <- mt_genes_ae[,c("V2","V3","Genes")]
mt_genes_ae$chr <- "chrM"
mt_genes_ae$value <- 1
mt_genes_ae <- mt_genes_ae[,c("chr","V2","V3","value","Genes")]
colnames(mt_genes_ae) <- c("chr","start","end","value","gene")
mt_genes_ae$start <- mt_genes_ae$start * 100000
mt_genes_ae$end <- mt_genes_ae$end * 100000
mt_genes_ae <- mt_genes_ae %>% dplyr::mutate_at(c("start","end","value"),as.numeric)

# chr_genes_ae <- df1_link_WGS_017_E20_F03
# chr_genes_ae$gene <- "Ecxample.2.1aa.2"

# anno_genes_ae <- rbind(mt_genes_ae,chr_genes_ae)
# 
# anno_genes_ae <- anno_genes_ae %>% dplyr::mutate_at(c("start","end","value"),as.numeric)

# run and commennt and re runn the one on top
mt_genes_ae <- read.table(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/Visualization_samples/mt_ae_gtf_genes_trna_rrna.txt",sep = "\t")
mt_genes_2 <- mt_genes_ae %>% dplyr::filter(V1 == "gene") %>% dplyr::select(V2,V3,V6) %>% dplyr::filter(V6 != "") %>% dplyr::mutate(chr="chrM",
                                                                                                                                    value = 1)
mt_genes_2 <- mt_genes_2[,c("chr","V2","V3","value","V6")]
colnames(mt_genes_2) <- c("chr","start","end","value","gene")
mt_genes_2$start <- mt_genes_2$start * 100000
mt_genes_2$end <- mt_genes_2$end * 100000
mt_genes_2 <- mt_genes_2 %>% dplyr::mutate_at(c("start","end","value"),as.numeric)

Apply circos for all samples

circos_RIL_plots <- lapply(dfs_numts_pos_match_len,function(df) {
  
  #png(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/",df[["V2"]][1],"_circos_chr_to_mt.png"),width = 680,height = 680)
  #svg(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/",df[["V2"]][1],"_circos_chr_to_mt.svg"),width = 10,height = 10)

  
  #run code for circos
  df_s <- df %>% dplyr::group_by(chr) %>%
    dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>%
    dplyr::arrange(V6) %>% 
    dplyr::select(chr,start,end,chr_matchLen,mt_start,mt_end,mt_matchLen) %>%
    dplyr::distinct()
  
  df_s$mt_start <- as.integer(df_s$mt_start)*100000
  df_s$mt_end <- as.integer(df_s$mt_end)*100000
  
  chr_df_s <- df_s %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::select(chr,start,end,chr_matchLen) %>% dplyr::mutate(chr=paste0("chr",chr))
  colnames(chr_df_s) <- c("chr","start","end","value")
  mt_df_s <- df_s %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::select(mt_start,mt_end,mt_matchLen)
  mt_df_s$chr <- "chrM"
  colnames(mt_df_s) <- c("chr","start","end","value")

  chr_df_s$start <- as.integer(chr_df_s$start)
  chr_df_s$end <- as.integer(chr_df_s$end)
  chr_mt_df_s <- dplyr::bind_rows(chr_df_s,mt_df_s)
  
  #Create the chr and mt regions of the numts by splitting them.

  df1_link <- chr_mt_df_s %>% dplyr::filter(chr %in% c("chr1","chr2","chr3"))
  df2_link <- chr_mt_df_s %>% dplyr::filter(chr %in% c("chrM")) 
  
  
  
  # Plot the circos
  #circos.par("track.height"=0.8, gap.degree=5, cell.padding=c(0, 0, 0, 0))
  circos.clear()
  circos.par(gap.degree=5)

  ref_fd_ae <- data.frame("Chromosome"=c("chr1","chr2","chr3","chrM"),"ChromStart"=c(0,0,0,0),"Chromend"=c(310827022,474425716,409777670,16790*100000))

  #circos.genomicInitialize(ref_fd_ae)
  circos.genomicInitialize(ref_fd_ae,plotType = NULL)
  circos.genomicLabels(mt_genes_ae ,labels.column = 5, cex=0.7,line_lwd=0.6, line_col="grey20", connection_height = 0.019,
                       labels_height = 0.2,niceFacing = TRUE,side = "outside")
  # circos.genomicLabels(mt_genes_2 ,labels.column = 5, cex=0.5,line_lwd=0.6, line_col="grey20", connection_height = 0.05, labels_height = 0.2,niceFacing = TRUE,side = "outside")

  circos.track(ylim=c(0, 1), panel.fun=function(x, y) {
    chr=CELL_META$sector.index
    xlim=CELL_META$xlim
    ylim=CELL_META$ylim
    circos.text(mean(xlim), mean(ylim), chr, cex=0.8)}, bg.col = c("#0000FF40","#0000FF40","#0000FF40","#FF000040"), bg.border=F, track.height=0.06)
  
  circos.track(track.index = get.current.track.index(),
               panel.fun = function(x, y) {
                 circos.genomicAxis(h = "bottom", direction = "inside",labels.cex = 0.6,tickLabelsStartFromZero = TRUE)})
  
  set_track_gap(gap = 0.04)
  
  # circos.genomicTrack(chr_mt_df_s %>% dplyr::distinct(),
  #                     track.height=0.1,
  #                     panel.fun = function(region, value, ...) {
  #                       circos.genomicPoints(region, value,
  #                                            pch = 6,
  #                                            cex = 1.6,
  #                                            col="black")})#col=ifelse(value[[1]] > 150,"red","black"))})
  circos.genomicTrack(chr_mt_df_s %>% dplyr::distinct(),
                      track.height=0.2,
                      panel.fun = function(region,value,...) {
                        circos.genomicRect(region, value,col = "#FF000040",...)})
                        #circos.genomicPoints(region, value,pch = 6,cex = 1.6,col="black")})#col=ifelse(value[[1]] > 150,"red","black"))})
  

  
  #circos.update(sector.index = "chrM",track.index = 4)
  #circos.points(x=col="red")
  
  col <- alpha(wes_palette("Zissou1", n = nrow(df2_link), type = "continuous"), 0.4)
  circos.genomicLink(df1_link %>%  dplyr::mutate_at(c("start","end","value"),as.numeric),
                     df2_link %>%  dplyr::mutate_at(c("start","end","value"),as.numeric),
                     #use lirbary wesanderson http://www.sthda.com/english/wiki/colors-in-r
                     col = col)
  # col = colorRampPalette(brewer.pal(5, "Dark2"))(nrow(df2_link_WGS_017_E20_F03)))#,border = NA,transparency=0.1)
  title(paste0(df[["V2"]][1]))
 # dev.off()

})

Adding missing grouping variables: `chr`
Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

Adding missing grouping variables: `chr`

save the table of the plots

#v5 is cluster of 500bp gaps
#v6 is cluster of more than 2 reads supporting the previous cluster
lapply(dfs_numts_pos_match_len,function(df) {
  df_s <- df %>% dplyr::group_by(chr) %>%
    dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>%
    dplyr::arrange(V6) %>% 
    dplyr::select(chr,start,end,chr_matchLen,mt_start,mt_end,mt_matchLen) %>%
    dplyr::distinct() %>% 
    write.table(paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/",df[["V2"]][1],"_chr_to_mt_table.csv"),sep = ",",col.names = TRUE,row.names = FALSE)
})

$WGS_017_E20_F03
NULL

$WGS_021_E20_F02
NULL

$WGS_024_E20_F02
NULL

$WGS_025_E20_F02
NULL

$WGS_038_E20_F07
NULL

$WGS_040_E20_F02
NULL

$WGS_042_E20_F02
NULL

$WGS_043_E20_F02
NULL

$WGS_047_E20_F02
NULL

$WGS_050_E20_F02
NULL

$WGS_050_E20_F03
NULL

$WGS_060_E20_F06
NULL

$WGS_063_E20_F02
NULL

$WGS_080_E20_F02
NULL

$WGS_084_E20_F02
NULL

$WGS_088_E20_F05
NULL

$WGS_088_E20_F06
NULL

$WGS_095_E20_F11
NULL

$WGS_115_E20_F03
NULL

$WGS_132_E20_F10
NULL

$WGS_134_E20_F02
NULL

$WGS_211_E20_F02
NULL

$WGS_220_E20_F07
NULL

$WGS_230_E20_F06
NULL

$WGS_240_E20_F05
NULL

$WGS_246_E20_F05
NULL

$WGS_246_E20_F07
NULL

$WGS_249_E20_F02
NULL

$WGS_252_E20_F02
NULL

$WGS_253_E20_F01
NULL

$WGS_262_E20_F07
NULL

$WGS_266_E20_F04
NULL

$WGS_289_E20_F02
NULL

$WGS_299_E20_F09
NULL

$WGS_300_E20_F05
NULL

karyotype plotter

# library(GenomicRanges)
# sapply(c("IRanges", "AnnotationDbi", "GenomicAlignments", "plyranges", "restfulr", "GenomicRanges", "Biostrings", "SummarizedExperiment", "BiocIO", "XVector", "Rsamtools", "rtracklayer", "DelayedArray", "GenomeInfoDb"), unloadNamespace)

library(plyranges)
mt_genes_2_ranges <- GenomicRanges::GRanges(seqnames = "NC_035159.1",ranges = IRanges::IRanges(start = mt_genes_2$start/100000,end = mt_genes_2$end/100000),genes=mt_genes_2$gene, y0=0,y1=0.13)
#mt_genes_2_ranges <- GRanges(seqnames = mt_genes_2$chr,ranges = IRanges(start = mt_genes_2$start/100000,end = mt_genes_2$end/100000),genes=mt_genes_2$gene, y0=0,y1=0.13)

Wrangle for each sample the genomic rannges

#library(S4Vectors)
# library(GenomicRanges)
# library(IRanges)
#library(karyoploteR)

list_of_regions <- lapply(dfs_numts_pos_match_len,function(df){
  
  cat(paste0("start with sample: ",df[["V2"]][1],"\n"))
  ranges_df <- GenomicRanges::GRanges(seqnames = df %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>% dplyr::filter(chr %in% c(1,2,3)) %>% dplyr::pull(chr),
                       ranges =IRanges::IRanges(start = as.numeric(df %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>%
                                                            dplyr::filter(chr %in% c(1,2,3)) %>%
                                                            dplyr::pull(mt_start)),
                                       end = as.numeric(df %>% dplyr::filter(chr_matchLen > 20 & mt_matchLen > 20) %>%
                                                          dplyr::filter(chr %in% c(1,2,3)) %>%
                                                          dplyr::pull(mt_end))),
                       real_chr="chrM")
  
  ranges_df <- as.data.frame(ranges_df)
  ranges_df$seqnames2 <- ranges_df$seqnames
  #Set the name of the chrm to the official ncbi nane that i use to create under the genes for overlapping.
  #ranges_df$seqnames <- "chrM"
  ranges_df$seqnames <- "NC_035159.1"
  ranges_df <- dplyr::mutate(ranges_df,
                             chr_start_end=paste0(seqnames,":",start,"-",end))
  
  cat("extracting regions\n")
  empty_gr <- GenomicRanges::GRanges(seqnames = character(), IRanges::IRanges(start = integer(), end = integer()))
  
  regs1 <- if(nrow(ranges_df %>% dplyr::filter(seqnames2 == 1)) != 0){
    toGRanges(ranges_df %>% filter(seqnames2 == 1) %>% dplyr::pull(chr_start_end))
  }else{
  empty_gr}
  
  regs2 <- if(nrow(ranges_df %>% dplyr::filter(seqnames2 == 2)) != 0){
    toGRanges(ranges_df %>% filter(seqnames2 == 2) %>% dplyr::pull(chr_start_end))
  }else{
  empty_gr}
  
  regs3 <- if(nrow(ranges_df %>% dplyr::filter(seqnames2 == 3)) != 0){
    toGRanges(ranges_df %>% filter(seqnames2 == 3) %>% dplyr::pull(chr_start_end))
  }else{
  empty_gr}
  
  regs1_df <- as.data.frame(regs1) %>% dplyr::mutate(chrom="chr1")
  regs2_df <- as.data.frame(regs2) %>% dplyr::mutate(chrom="chr2")
  regs3_df <- as.data.frame(regs3) %>% dplyr::mutate(chrom="chr3")
  regs_df <- rbind(regs1_df,regs2_df,regs3_df)
  cat("done\n")
  return(regs_df)})

start with sample: WGS_017_E20_F03
extracting regions
done
start with sample: WGS_021_E20_F02
extracting regions
done
start with sample: WGS_024_E20_F02
extracting regions
done
start with sample: WGS_025_E20_F02
extracting regions
done
start with sample: WGS_038_E20_F07
extracting regions
done
start with sample: WGS_040_E20_F02
extracting regions
done
start with sample: WGS_042_E20_F02
extracting regions
done
start with sample: WGS_043_E20_F02
extracting regions
done
start with sample: WGS_047_E20_F02
extracting regions
done
start with sample: WGS_050_E20_F02
extracting regions
done
start with sample: WGS_050_E20_F03
extracting regions
done
start with sample: WGS_060_E20_F06
extracting regions
done
start with sample: WGS_063_E20_F02
extracting regions
done
start with sample: WGS_080_E20_F02
extracting regions
done
start with sample: WGS_084_E20_F02
extracting regions
done
start with sample: WGS_088_E20_F05
extracting regions
done
start with sample: WGS_088_E20_F06
extracting regions
done
start with sample: WGS_095_E20_F11
extracting regions
done
start with sample: WGS_115_E20_F03
extracting regions
done
start with sample: WGS_132_E20_F10
extracting regions
done
start with sample: WGS_134_E20_F02
extracting regions
done
start with sample: WGS_211_E20_F02
extracting regions
done
start with sample: WGS_220_E20_F07
extracting regions
done
start with sample: WGS_230_E20_F06
extracting regions
done
start with sample: WGS_240_E20_F05
extracting regions
done
start with sample: WGS_246_E20_F05
extracting regions
done
start with sample: WGS_246_E20_F07
extracting regions
done
start with sample: WGS_249_E20_F02
extracting regions
done
start with sample: WGS_252_E20_F02
extracting regions
done
start with sample: WGS_253_E20_F01
extracting regions
done
start with sample: WGS_262_E20_F07
extracting regions
done
start with sample: WGS_266_E20_F04
extracting regions
done
start with sample: WGS_289_E20_F02
extracting regions
done
start with sample: WGS_299_E20_F09
extracting regions
done
start with sample: WGS_300_E20_F05
extracting regions
done

library(S4Vectors)



lapply(names(list_of_regions),function(kt){
  
  #svg(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/karytypePlot_",kt,"numts_chr.svg"),width = 12,height = 4)
  
  custom.genome <- toGRanges(data.frame(chr=c("NC_035159.1"),start=c(0),end=c(16790)))
  kp <- plotKaryotype(genome = custom.genome)
  kpDataBackground(kp, r0 = 0,r1 = 0.25)
  kpDataBackground(kp, r0 = 0.25,r1 = 0.5,col="#FF000040")
  kpDataBackground(kp, r0 = 0.5,r1 = 0.75,col="#FF000040")
  kpDataBackground(kp, r0 = 0.75,r1 = 1,col="#FF000040")
  
  
   
  
  
  kpRect(kp,mt_genes_2_ranges,col="red")
  kpText(karyoplot = kp,data = mt_genes_2_ranges,labels = mt_genes_2_ranges$genes,y = 0.17,cex=0.6,col="red")
  
  #add mt sequences from  each chromoosome
  #add numts from chr 1
  kpRect(kp,data = list_of_regions[[kt]] %>% filter(chrom == "chr1") %>% toGRanges, y0 = 0.3,y1=0.4) 
  #add numts from chr 2
  kpRect(kp,data = list_of_regions[[kt]] %>% filter(chrom == "chr2") %>% toGRanges, y0 = 0.6,y1 = 0.7)
  #add numts from chr 3
  kpRect(kp,data = list_of_regions[[kt]] %>% filter(chrom == "chr3") %>% toGRanges, y0 = 0.8,y1 = 0.9)
  title(paste0(kt))
  
  #dev.off()
    
})

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list_of_regions_ID <- lapply(names(list_of_regions),function(name) {
  df <- list_of_regions[[name]]
  df$ID <- name
  return(df)
})

svg(filename = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/Postions_in_CHR_of_numts.svg",width = 10,height = 32)
combined_list_of_regions_ID <- do.call(rbind,list_of_regions_ID)
# combined_list_of_regions_ID <- dplyr::mutate(combined_list_of_regions_ID,pos_chr_start_end = paste0(chrom,":",start,"-",end))
combined_list_of_regions_ID <- dplyr::mutate(combined_list_of_regions_ID,pos_chr_start_end = paste0(seqnames,":",start,"-",end))

table(combined_list_of_regions_ID$pos_chr_start_end) %>% subset(combined_list_of_regions_ID$pos_chr_start_end > 1)

     NC_035159.1:1-3230        NC_035159.1:1-35        NC_035159.1:1-40        NC_035159.1:1-47        NC_035159.1:1-55      NC_035159.1:10-131 NC_035159.1:10054-12188 
                      1                       1                       1                       1                       2                       1                       1 
NC_035159.1:10079-10409 NC_035159.1:10086-10431 NC_035159.1:10098-10376 NC_035159.1:10141-10227 NC_035159.1:10141-10238 NC_035159.1:10141-10283 NC_035159.1:10145-10367 
                      1                       1                       1                       1                       1                       2                       1 
NC_035159.1:10153-10231 NC_035159.1:10154-10294 NC_035159.1:10170-11055 NC_035159.1:10197-11104 NC_035159.1:10217-10266 NC_035159.1:10218-10265 NC_035159.1:10228-10249 
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                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:6443-6671   NC_035159.1:6456-6712   NC_035159.1:6525-7075   NC_035159.1:6539-7178   NC_035159.1:6552-6715   NC_035159.1:6573-6741   NC_035159.1:6621-7011 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:6622-6824   NC_035159.1:6645-7097   NC_035159.1:6657-8534   NC_035159.1:6658-6813   NC_035159.1:6659-6709   NC_035159.1:6663-6727   NC_035159.1:6664-6696 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:6664-6729   NC_035159.1:6664-6733   NC_035159.1:6665-6710   NC_035159.1:6665-6964   NC_035159.1:6666-6732   NC_035159.1:6666-6964   NC_035159.1:6667-6688 
                      1                       1                       1                       2                       1                       1                       1 
  NC_035159.1:6667-6701   NC_035159.1:6668-7498   NC_035159.1:6669-6723   NC_035159.1:6670-6721   NC_035159.1:6677-6703   NC_035159.1:6696-6723   NC_035159.1:6704-6742 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:6711-6767   NC_035159.1:6721-7098   NC_035159.1:6730-6826   NC_035159.1:6737-8610    NC_035159.1:680-1777   NC_035159.1:6812-7416   NC_035159.1:6844-7239 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:6888-7257   NC_035159.1:7041-7151    NC_035159.1:706-1187   NC_035159.1:7073-7142   NC_035159.1:7077-7168     NC_035159.1:708-956     NC_035159.1:709-881 
                      1                       1                       1                       1                       1                       1                       1 
    NC_035159.1:717-842     NC_035159.1:719-861     NC_035159.1:723-782     NC_035159.1:725-870   NC_035159.1:7289-7845   NC_035159.1:7291-8783   NC_035159.1:7292-7582 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7335-7400   NC_035159.1:7335-7498   NC_035159.1:7335-7529   NC_035159.1:7335-7538   NC_035159.1:7335-7578   NC_035159.1:7335-7617   NC_035159.1:7335-7669 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7364-7605   NC_035159.1:7366-7544   NC_035159.1:7369-7625   NC_035159.1:7370-7487   NC_035159.1:7370-7621   NC_035159.1:7372-7642   NC_035159.1:7373-7481 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7373-7551   NC_035159.1:7375-7659   NC_035159.1:7379-7495     NC_035159.1:738-824   NC_035159.1:7381-7484   NC_035159.1:7381-7565   NC_035159.1:7383-7405 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7383-7659   NC_035159.1:7384-7624   NC_035159.1:7386-7426   NC_035159.1:7386-7680   NC_035159.1:7401-7587   NC_035159.1:7434-8969   NC_035159.1:7440-7652 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7444-7472   NC_035159.1:7447-7713   NC_035159.1:7458-7639   NC_035159.1:7493-8264     NC_035159.1:75-2582   NC_035159.1:7509-7839   NC_035159.1:7520-7930 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7533-7624   NC_035159.1:7540-7590   NC_035159.1:7566-7860   NC_035159.1:7607-7953   NC_035159.1:7612-7636   NC_035159.1:7729-8212   NC_035159.1:7753-7937 
                      1                       1                       1                       1                       1                       1                       1 
 NC_035159.1:7757-12015     NC_035159.1:784-829     NC_035159.1:784-835     NC_035159.1:784-838     NC_035159.1:784-860   NC_035159.1:7840-7866   NC_035159.1:7840-7882 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:7840-7909   NC_035159.1:7840-7988   NC_035159.1:7860-8158   NC_035159.1:7915-9001     NC_035159.1:796-850      NC_035159.1:80-119      NC_035159.1:80-135 
                      1                       1                       1                       1                       1                       1                       1 
     NC_035159.1:80-141      NC_035159.1:80-150      NC_035159.1:80-205      NC_035159.1:80-243   NC_035159.1:8000-8231   NC_035159.1:8000-8240     NC_035159.1:805-838 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:8062-8776   NC_035159.1:8064-8873     NC_035159.1:808-830     NC_035159.1:808-832     NC_035159.1:808-836     NC_035159.1:808-838     NC_035159.1:808-841 
                      1                       1                       1                       1                       2                       1                       1 
    NC_035159.1:808-846     NC_035159.1:808-849   NC_035159.1:8133-8249   NC_035159.1:8135-8226   NC_035159.1:8138-8276   NC_035159.1:8144-8212   NC_035159.1:8146-8171 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:8168-8306   NC_035159.1:8181-8276   NC_035159.1:8183-8224   NC_035159.1:8184-8224   NC_035159.1:8209-8330   NC_035159.1:8220-8258    NC_035159.1:823-2546 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:8300-8322   NC_035159.1:8439-8816   NC_035159.1:8553-8788   NC_035159.1:8603-8764   NC_035159.1:8757-9442   NC_035159.1:8804-9256   NC_035159.1:8808-9104 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:8808-9856   NC_035159.1:8819-9975   NC_035159.1:8897-9390  NC_035159.1:8908-10196   NC_035159.1:8981-9014   NC_035159.1:8984-9217   NC_035159.1:8988-9181 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:8990-9224   NC_035159.1:8994-9094       NC_035159.1:9-220   NC_035159.1:9001-9088   NC_035159.1:9005-9188   NC_035159.1:9019-9282   NC_035159.1:9022-9080 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:9040-9197   NC_035159.1:9040-9234   NC_035159.1:9058-9181   NC_035159.1:9069-9225   NC_035159.1:9071-9157    NC_035159.1:910-1376   NC_035159.1:9164-9280 
                      1                       1                       1                       1                       1                       1                       1 
   NC_035159.1:927-1303   NC_035159.1:9272-9339  NC_035159.1:9274-12128   NC_035159.1:9329-9419  NC_035159.1:9374-11878   NC_035159.1:9398-9526   NC_035159.1:9469-9528 
                      1                       1                       1                       1                       1                       1                       1 
 NC_035159.1:9489-11210   NC_035159.1:9520-9778   NC_035159.1:9524-9948   NC_035159.1:9530-9556   NC_035159.1:9538-9560   NC_035159.1:9538-9628   NC_035159.1:9539-9614 
                      1                       1                       1                       1                       1                       1                       1 
  NC_035159.1:9542-9725   NC_035159.1:9548-9653   NC_035159.1:9550-9690   NC_035159.1:9558-9639   NC_035159.1:9582-9638   NC_035159.1:9603-9702   NC_035159.1:9611-9979 
                      1                       1                       1                       1                       1                       1                       1 
 NC_035159.1:9621-10162   NC_035159.1:9665-9870                    <NA>                    <NA>                    <NA>                    <NA>                    <NA> 
                      1                       1                                                                                                                         
                   <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA> 
                                                                                                                                                                        
                   <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA> 
                                                                                                                                                                        
                   <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA> 
                                                                                                                                                                        
                   <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA>                    <NA> 
                                                                                                                                                                        

combined_list_of_regions_ID %>% dplyr::group_by(pos_chr_start_end) %>% dplyr::mutate(counts_seqs=n()) %>% dplyr::filter(counts_seqs > 1)

library(ggplot2)

pp <- ggplot(combined_list_of_regions_ID %>% dplyr::group_by(pos_chr_start_end) %>% dplyr::arrange(.by_group = TRUE) %>% dplyr::mutate(counts_seqs=n()) %>% dplyr::filter(counts_seqs > 1),
       aes(x = pos_chr_start_end,
           y = as.factor(chrom),
           color=pos_chr_start_end)) + 
  
  #geom_histogram(stat="count") + #binwidth = 1) + 
  #geom_point(color="red") +
  geom_point(position = position_jitter(width = 0.25,height = 0.25,seed = 123456),size=3,alpha=0.5) +
  
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  #scale_y_discrete(chrom) +
  #coord_flip() +
  #cut("chr") +
  #geom_text(stat = "count", aes(label = ifelse(count >= 2, count, "")), vjust = -0.25) + 
  xlab("Position") + 
  ylab("Count") +
  facet_wrap(~ID,ncol=1) +
  ggtitle("Points of NUMT(s) Positions")

pp
dev.off()

null device 
          1 

write.table(combined_list_of_regions_ID %>% dplyr::group_by(pos_chr_start_end) %>% dplyr::arrange(.by_group = TRUE) %>% dplyr::mutate(counts_seqs=n()),file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/Postions_in_CHR_of_numts.csv",sep = ",")

Counnt overlaps by sample and all together and plot CIRCOS

Per sample

library(plyranges)
names(list_of_regions)

 [1] "WGS_017_E20_F03" "WGS_021_E20_F02" "WGS_024_E20_F02" "WGS_025_E20_F02" "WGS_038_E20_F07" "WGS_040_E20_F02" "WGS_042_E20_F02" "WGS_043_E20_F02" "WGS_047_E20_F02" "WGS_050_E20_F02"
[11] "WGS_050_E20_F03" "WGS_060_E20_F06" "WGS_063_E20_F02" "WGS_080_E20_F02" "WGS_084_E20_F02" "WGS_088_E20_F05" "WGS_088_E20_F06" "WGS_095_E20_F11" "WGS_115_E20_F03" "WGS_132_E20_F10"
[21] "WGS_134_E20_F02" "WGS_211_E20_F02" "WGS_220_E20_F07" "WGS_230_E20_F06" "WGS_240_E20_F05" "WGS_246_E20_F05" "WGS_246_E20_F07" "WGS_249_E20_F02" "WGS_252_E20_F02" "WGS_253_E20_F01"
[31] "WGS_262_E20_F07" "WGS_266_E20_F04" "WGS_289_E20_F02" "WGS_299_E20_F09" "WGS_300_E20_F05"

list_of_regions_ID

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#https://bioconductor.org/packages/devel/bioc/vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(GenomicRanges)
#Downloaded from NCBI
gff_ae_txdb <- makeTxDbFromGFF(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/GCF_002204515.2_AaegL5.0_genomic.gff",format = "gff3",dataSource = "NCBI",organism = "Aedes aegypti",)

Import genomic features from the file as a GRanges object ... Warning in for (k in seq_along(lens)) { :
  closing unused connection 3 (/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Data/GCF_002204515.2_AaegL5.0_genomic.gff)
OK
Prepare the 'metadata' data frame ... OK
Make the TxDb object ... Warning in .extract_transcripts_from_GRanges(tx_IDX, gr, mcols0$type, mcols0$ID,  :
  some transcripts have no "transcript_id" attribute ==> their name ("tx_name" column in the TxDb object) was set to NA
Warning in .extract_transcripts_from_GRanges(tx_IDX, gr, mcols0$type, mcols0$ID,  :
  the transcript names ("tx_name" column in the TxDb object) imported from the "transcript_id" attribute are not unique
OK

#This were selected using cut,awk,grep by command line
genes(gff_ae_txdb)

GRanges object with 19623 ranges and 1 metadata column:
                 seqnames            ranges strand |     gene_id
                    <Rle>         <IRanges>  <Rle> | <character>
  CFI06_mgp01 NC_035159.1       11547-12488      - | CFI06_mgp01
  CFI06_mgp02 NC_035159.1       10323-11457      + | CFI06_mgp02
  CFI06_mgp03 NC_035159.1        9798-10319      + | CFI06_mgp03
  CFI06_mgp04 NC_035159.1         9364-9660      - | CFI06_mgp04
  CFI06_mgp05 NC_035159.1         8027-9370      - | CFI06_mgp05
          ...         ...               ...    ... .         ...
  Trnay-gua-5 NC_035108.1 14298210-14298304      + | Trnay-gua-5
  Trnay-gua-6 NC_035108.1 15183501-15183595      + | Trnay-gua-6
  Trnay-gua-7 NC_035108.1 47870658-47870755      + | Trnay-gua-7
  Trnay-gua-8 NC_035108.1 47871172-47871265      + | Trnay-gua-8
  Trnay-gua-9 NC_035108.1 47934762-47934891      - | Trnay-gua-9
  -------
  seqinfo: 798 sequences from an unspecified genome; no seqlengths

head(seqlevels(gff_ae_txdb))

[1] "NC_035107.1"    "NC_035108.1"    "NC_035109.1"    "NC_035159.1"    "NW_018734409.1" "NW_018734412.1"

#select only mt
columns(gff_ae_txdb)

 [1] "CDSCHROM"   "CDSEND"     "CDSID"      "CDSNAME"    "CDSPHASE"   "CDSSTART"   "CDSSTRAND"  "EXONCHROM"  "EXONEND"    "EXONID"     "EXONNAME"   "EXONRANK"   "EXONSTART"  "EXONSTRAND"
[15] "GENEID"     "TXCHROM"    "TXEND"      "TXID"       "TXNAME"     "TXSTART"    "TXSTRAND"   "TXTYPE"    

seqlevels(gff_ae_txdb) <- "NC_035159.1"
#from the fasta  /locus_tag="CFI06_mgp11"                      /db_xref="GeneID:33307558"      CDS             2903..3587                      /gene="COX2" 
#cox2,atp8,atp6,cox3
#gene names are not correctly extrracted ffrom the gff file
keys_2_aaeg <- c("CFI06_mgp12","CFI06_mgp10","CFI06_mgp09","CFI06_mgp08") 
keytypes(gff_ae_txdb)

[1] "CDSID"    "CDSNAME"  "EXONID"   "EXONNAME" "GENEID"   "TXID"     "TXNAME"  

columns(gff_ae_txdb)

 [1] "CDSCHROM"   "CDSEND"     "CDSID"      "CDSNAME"    "CDSPHASE"   "CDSSTART"   "CDSSTRAND"  "EXONCHROM"  "EXONEND"    "EXONID"     "EXONNAME"   "EXONRANK"   "EXONSTART"  "EXONSTRAND"
[15] "GENEID"     "TXCHROM"    "TXEND"      "TXID"       "TXNAME"     "TXSTART"    "TXSTRAND"   "TXTYPE"    

keys(gff_ae_txdb)

   [1] "CFI06_mgp01"  "CFI06_mgp02"  "CFI06_mgp03"  "CFI06_mgp04"  "CFI06_mgp05"  "CFI06_mgp06"  "CFI06_mgp07"  "CFI06_mgp08"  "CFI06_mgp09"  "CFI06_mgp10"  "CFI06_mgp11"  "CFI06_mgp12" 
  [13] "CFI06_mgp13"  "CFI06_mgr01"  "CFI06_mgr02"  "CFI06_mgt01"  "CFI06_mgt02"  "CFI06_mgt03"  "CFI06_mgt04"  "CFI06_mgt05"  "CFI06_mgt06"  "CFI06_mgt07"  "CFI06_mgt08"  "CFI06_mgt09" 
  [25] "CFI06_mgt10"  "CFI06_mgt11"  "CFI06_mgt12"  "CFI06_mgt13"  "CFI06_mgt14"  "CFI06_mgt15"  "CFI06_mgt16"  "CFI06_mgt17"  "CFI06_mgt18"  "CFI06_mgt19"  "CFI06_mgt20"  "CFI06_mgt21" 
  [37] "CFI06_mgt22"  "CFI06_mgt23"  "LOC110673976" "LOC110673977" "LOC110673978" "LOC110673979" "LOC110673980" "LOC110673981" "LOC110673982" "LOC110673983" "LOC110673984" "LOC110673985"
  [49] "LOC110673986" "LOC110673987" "LOC110673988" "LOC110673989" "LOC110673990" "LOC110673991" "LOC110673992" "LOC110673993" "LOC110673994" "LOC110673995" "LOC110673996" "LOC110673997"
  [61] "LOC110673998" "LOC110673999" "LOC110674000" "LOC110674001" "LOC110674002" "LOC110674003" "LOC110674004" "LOC110674005" "LOC110674006" "LOC110674007" "LOC110674008" "LOC110674009"
  [73] "LOC110674010" "LOC110674011" "LOC110674012" "LOC110674013" "LOC110674014" "LOC110674015" "LOC110674016" "LOC110674017" "LOC110674018" "LOC110674019" "LOC110674020" "LOC110674021"
  [85] "LOC110674022" "LOC110674023" "LOC110674024" "LOC110674025" "LOC110674026" "LOC110674027" "LOC110674028" "LOC110674029" "LOC110674030" "LOC110674031" "LOC110674032" "LOC110674033"
  [97] "LOC110674034" "LOC110674035" "LOC110674036" "LOC110674037" "LOC110674038" "LOC110674039" "LOC110674040" "LOC110674041" "LOC110674042" "LOC110674043" "LOC110674044" "LOC110674045"
 [109] "LOC110674046" "LOC110674047" "LOC110674048" "LOC110674049" "LOC110674050" "LOC110674051" "LOC110674052" "LOC110674053" "LOC110674054" "LOC110674055" "LOC110674056" "LOC110674057"
 [121] "LOC110674058" "LOC110674059" "LOC110674060" "LOC110674061" "LOC110674062" "LOC110674063" "LOC110674064" "LOC110674065" "LOC110674066" "LOC110674067" "LOC110674068" "LOC110674069"
 [133] "LOC110674070" "LOC110674071" "LOC110674072" "LOC110674073" "LOC110674074" "LOC110674075" "LOC110674076" "LOC110674077" "LOC110674078" "LOC110674079" "LOC110674080" "LOC110674081"
 [145] "LOC110674082" "LOC110674083" "LOC110674084" "LOC110674085" "LOC110674086" "LOC110674087" "LOC110674088" "LOC110674089" "LOC110674090" "LOC110674091" "LOC110674092" "LOC110674093"
 [157] "LOC110674094" "LOC110674095" "LOC110674096" "LOC110674097" "LOC110674098" "LOC110674099" "LOC110674100" "LOC110674101" "LOC110674102" "LOC110674103" "LOC110674104" "LOC110674105"
 [169] "LOC110674106" "LOC110674107" "LOC110674108" "LOC110674109" "LOC110674110" "LOC110674111" "LOC110674112" "LOC110674113" "LOC110674114" "LOC110674115" "LOC110674116" "LOC110674117"
 [181] "LOC110674118" "LOC110674119" "LOC110674120" "LOC110674121" "LOC110674122" "LOC110674123" "LOC110674124" "LOC110674125" "LOC110674126" "LOC110674127" "LOC110674128" "LOC110674129"
 [193] "LOC110674130" "LOC110674131" "LOC110674132" "LOC110674133" "LOC110674134" "LOC110674135" "LOC110674136" "LOC110674137" "LOC110674138" "LOC110674139" "LOC110674140" "LOC110674141"
 [205] "LOC110674142" "LOC110674143" "LOC110674144" "LOC110674145" "LOC110674146" "LOC110674147" "LOC110674148" "LOC110674149" "LOC110674150" "LOC110674151" "LOC110674152" "LOC110674153"
 [217] "LOC110674154" "LOC110674155" "LOC110674156" "LOC110674157" "LOC110674158" "LOC110674159" "LOC110674160" "LOC110674161" "LOC110674162" "LOC110674163" "LOC110674164" "LOC110674165"
 [229] "LOC110674166" "LOC110674167" "LOC110674168" "LOC110674169" "LOC110674170" "LOC110674171" "LOC110674172" "LOC110674173" "LOC110674174" "LOC110674175" "LOC110674176" "LOC110674177"
 [241] "LOC110674178" "LOC110674179" "LOC110674180" "LOC110674181" "LOC110674182" "LOC110674183" "LOC110674184" "LOC110674185" "LOC110674186" "LOC110674187" "LOC110674188" "LOC110674189"
 [253] "LOC110674190" "LOC110674191" "LOC110674192" "LOC110674193" "LOC110674194" "LOC110674195" "LOC110674196" "LOC110674197" "LOC110674198" "LOC110674199" "LOC110674200" "LOC110674201"
 [265] "LOC110674202" "LOC110674203" "LOC110674204" "LOC110674205" "LOC110674206" "LOC110674207" "LOC110674208" "LOC110674209" "LOC110674210" "LOC110674211" "LOC110674212" "LOC110674213"
 [277] "LOC110674214" "LOC110674215" "LOC110674216" "LOC110674217" "LOC110674218" "LOC110674219" "LOC110674220" "LOC110674221" "LOC110674222" "LOC110674223" "LOC110674224" "LOC110674225"
 [289] "LOC110674226" "LOC110674227" "LOC110674228" "LOC110674229" "LOC110674230" "LOC110674231" "LOC110674232" "LOC110674233" "LOC110674234" "LOC110674235" "LOC110674236" "LOC110674237"
 [301] "LOC110674238" "LOC110674239" "LOC110674240" "LOC110674241" "LOC110674242" "LOC110674243" "LOC110674244" "LOC110674245" "LOC110674246" "LOC110674247" "LOC110674248" "LOC110674249"
 [313] "LOC110674250" "LOC110674251" "LOC110674252" "LOC110674253" "LOC110674254" "LOC110674255" "LOC110674256" "LOC110674257" "LOC110674258" "LOC110674259" "LOC110674260" "LOC110674261"
 [325] "LOC110674262" "LOC110674263" "LOC110674264" "LOC110674265" "LOC110674266" "LOC110674267" "LOC110674268" "LOC110674269" "LOC110674270" "LOC110674271" "LOC110674272" "LOC110674273"
 [337] "LOC110674274" "LOC110674275" "LOC110674276" "LOC110674277" "LOC110674278" "LOC110674279" "LOC110674280" "LOC110674281" "LOC110674282" "LOC110674283" "LOC110674284" "LOC110674285"
 [349] "LOC110674286" "LOC110674287" "LOC110674288" "LOC110674289" "LOC110674290" "LOC110674291" "LOC110674292" "LOC110674293" "LOC110674294" "LOC110674295" "LOC110674296" "LOC110674297"
 [361] "LOC110674298" "LOC110674299" "LOC110674300" "LOC110674301" "LOC110674302" "LOC110674303" "LOC110674304" "LOC110674305" "LOC110674306" "LOC110674307" "LOC110674308" "LOC110674309"
 [373] "LOC110674310" "LOC110674311" "LOC110674312" "LOC110674313" "LOC110674314" "LOC110674315" "LOC110674316" "LOC110674317" "LOC110674318" "LOC110674319" "LOC110674320" "LOC110674321"
 [385] "LOC110674322" "LOC110674323" "LOC110674324" "LOC110674325" "LOC110674326" "LOC110674327" "LOC110674328" "LOC110674329" "LOC110674330" "LOC110674331" "LOC110674332" "LOC110674333"
 [397] "LOC110674334" "LOC110674335" "LOC110674336" "LOC110674337" "LOC110674338" "LOC110674339" "LOC110674340" "LOC110674341" "LOC110674342" "LOC110674343" "LOC110674344" "LOC110674345"
 [409] "LOC110674346" "LOC110674347" "LOC110674348" "LOC110674349" "LOC110674350" "LOC110674351" "LOC110674352" "LOC110674353" "LOC110674354" "LOC110674355" "LOC110674356" "LOC110674357"
 [421] "LOC110674358" "LOC110674359" "LOC110674360" "LOC110674361" "LOC110674362" "LOC110674363" "LOC110674364" "LOC110674365" "LOC110674366" "LOC110674367" "LOC110674368" "LOC110674369"
 [433] "LOC110674370" "LOC110674371" "LOC110674372" "LOC110674373" "LOC110674374" "LOC110674375" "LOC110674376" "LOC110674377" "LOC110674378" "LOC110674379" "LOC110674380" "LOC110674381"
 [445] "LOC110674382" "LOC110674383" "LOC110674384" "LOC110674385" "LOC110674386" "LOC110674387" "LOC110674388" "LOC110674389" "LOC110674390" "LOC110674391" "LOC110674392" "LOC110674393"
 [457] "LOC110674394" "LOC110674395" "LOC110674396" "LOC110674397" "LOC110674398" "LOC110674399" "LOC110674400" "LOC110674401" "LOC110674402" "LOC110674403" "LOC110674404" "LOC110674405"
 [469] "LOC110674406" "LOC110674407" "LOC110674408" "LOC110674409" "LOC110674410" "LOC110674411" "LOC110674412" "LOC110674413" "LOC110674414" "LOC110674415" "LOC110674416" "LOC110674417"
 [481] "LOC110674418" "LOC110674419" "LOC110674420" "LOC110674421" "LOC110674422" "LOC110674423" "LOC110674424" "LOC110674425" "LOC110674426" "LOC110674427" "LOC110674428" "LOC110674429"
 [493] "LOC110674430" "LOC110674431" "LOC110674432" "LOC110674433" "LOC110674434" "LOC110674435" "LOC110674436" "LOC110674437" "LOC110674438" "LOC110674439" "LOC110674440" "LOC110674441"
 [505] "LOC110674442" "LOC110674443" "LOC110674444" "LOC110674445" "LOC110674446" "LOC110674447" "LOC110674448" "LOC110674449" "LOC110674450" "LOC110674451" "LOC110674452" "LOC110674453"
 [517] "LOC110674454" "LOC110674455" "LOC110674456" "LOC110674457" "LOC110674458" "LOC110674459" "LOC110674460" "LOC110674461" "LOC110674462" "LOC110674463" "LOC110674464" "LOC110674465"
 [529] "LOC110674466" "LOC110674467" "LOC110674468" "LOC110674469" "LOC110674470" "LOC110674471" "LOC110674472" "LOC110674473" "LOC110674474" "LOC110674475" "LOC110674476" "LOC110674477"
 [541] "LOC110674478" "LOC110674479" "LOC110674480" "LOC110674481" "LOC110674482" "LOC110674483" "LOC110674484" "LOC110674485" "LOC110674486" "LOC110674487" "LOC110674488" "LOC110674489"
 [553] "LOC110674490" "LOC110674491" "LOC110674492" "LOC110674493" "LOC110674494" "LOC110674495" "LOC110674496" "LOC110674497" "LOC110674498" "LOC110674499" "LOC110674500" "LOC110674501"
 [565] "LOC110674502" "LOC110674503" "LOC110674504" "LOC110674505" "LOC110674506" "LOC110674507" "LOC110674508" "LOC110674509" "LOC110674510" "LOC110674511" "LOC110674512" "LOC110674513"
 [577] "LOC110674514" "LOC110674515" "LOC110674516" "LOC110674517" "LOC110674518" "LOC110674519" "LOC110674520" "LOC110674521" "LOC110674522" "LOC110674523" "LOC110674524" "LOC110674525"
 [589] "LOC110674526" "LOC110674527" "LOC110674528" "LOC110674529" "LOC110674530" "LOC110674531" "LOC110674532" "LOC110674533" "LOC110674534" "LOC110674535" "LOC110674536" "LOC110674537"
 [601] "LOC110674538" "LOC110674539" "LOC110674540" "LOC110674541" "LOC110674542" "LOC110674543" "LOC110674544" "LOC110674545" "LOC110674546" "LOC110674547" "LOC110674548" "LOC110674549"
 [613] "LOC110674550" "LOC110674551" "LOC110674552" "LOC110674553" "LOC110674554" "LOC110674555" "LOC110674556" "LOC110674557" "LOC110674558" "LOC110674559" "LOC110674560" "LOC110674561"
 [625] "LOC110674562" "LOC110674563" "LOC110674564" "LOC110674565" "LOC110674566" "LOC110674567" "LOC110674568" "LOC110674569" "LOC110674570" "LOC110674571" "LOC110674572" "LOC110674573"
 [637] "LOC110674574" "LOC110674575" "LOC110674576" "LOC110674577" "LOC110674578" "LOC110674579" "LOC110674580" "LOC110674581" "LOC110674582" "LOC110674583" "LOC110674584" "LOC110674585"
 [649] "LOC110674586" "LOC110674587" "LOC110674588" "LOC110674589" "LOC110674590" "LOC110674591" "LOC110674592" "LOC110674593" "LOC110674594" "LOC110674595" "LOC110674596" "LOC110674597"
 [661] "LOC110674598" "LOC110674599" "LOC110674600" "LOC110674601" "LOC110674602" "LOC110674603" "LOC110674604" "LOC110674605" "LOC110674606" "LOC110674607" "LOC110674608" "LOC110674609"
 [673] "LOC110674610" "LOC110674611" "LOC110674612" "LOC110674613" "LOC110674614" "LOC110674615" "LOC110674616" "LOC110674617" "LOC110674618" "LOC110674619" "LOC110674620" "LOC110674621"
 [685] "LOC110674622" "LOC110674623" "LOC110674624" "LOC110674625" "LOC110674626" "LOC110674627" "LOC110674628" "LOC110674629" "LOC110674630" "LOC110674631" "LOC110674632" "LOC110674633"
 [697] "LOC110674634" "LOC110674635" "LOC110674636" "LOC110674637" "LOC110674638" "LOC110674639" "LOC110674640" "LOC110674641" "LOC110674642" "LOC110674643" "LOC110674644" "LOC110674645"
 [709] "LOC110674646" "LOC110674647" "LOC110674648" "LOC110674649" "LOC110674650" "LOC110674651" "LOC110674652" "LOC110674653" "LOC110674654" "LOC110674655" "LOC110674656" "LOC110674657"
 [721] "LOC110674658" "LOC110674659" "LOC110674660" "LOC110674661" "LOC110674662" "LOC110674663" "LOC110674664" "LOC110674665" "LOC110674666" "LOC110674667" "LOC110674668" "LOC110674669"
 [733] "LOC110674670" "LOC110674671" "LOC110674672" "LOC110674673" "LOC110674674" "LOC110674675" "LOC110674676" "LOC110674677" "LOC110674678" "LOC110674679" "LOC110674680" "LOC110674681"
 [745] "LOC110674682" "LOC110674683" "LOC110674684" "LOC110674685" "LOC110674686" "LOC110674687" "LOC110674688" "LOC110674689" "LOC110674690" "LOC110674691" "LOC110674692" "LOC110674693"
 [757] "LOC110674694" "LOC110674695" "LOC110674696" "LOC110674697" "LOC110674698" "LOC110674699" "LOC110674700" "LOC110674701" "LOC110674702" "LOC110674703" "LOC110674704" "LOC110674705"
 [769] "LOC110674706" "LOC110674707" "LOC110674708" "LOC110674709" "LOC110674710" "LOC110674711" "LOC110674712" "LOC110674713" "LOC110674714" "LOC110674715" "LOC110674716" "LOC110674717"
 [781] "LOC110674718" "LOC110674719" "LOC110674720" "LOC110674721" "LOC110674722" "LOC110674723" "LOC110674724" "LOC110674725" "LOC110674726" "LOC110674727" "LOC110674728" "LOC110674729"
 [793] "LOC110674730" "LOC110674731" "LOC110674732" "LOC110674733" "LOC110674734" "LOC110674735" "LOC110674736" "LOC110674737" "LOC110674738" "LOC110674739" "LOC110674740" "LOC110674741"
 [805] "LOC110674742" "LOC110674743" "LOC110674744" "LOC110674745" "LOC110674746" "LOC110674747" "LOC110674748" "LOC110674749" "LOC110674750" "LOC110674751" "LOC110674752" "LOC110674753"
 [817] "LOC110674754" "LOC110674755" "LOC110674756" "LOC110674757" "LOC110674758" "LOC110674759" "LOC110674760" "LOC110674761" "LOC110674762" "LOC110674763" "LOC110674764" "LOC110674765"
 [829] "LOC110674766" "LOC110674767" "LOC110674768" "LOC110674769" "LOC110674770" "LOC110674771" "LOC110674772" "LOC110674773" "LOC110674774" "LOC110674775" "LOC110674776" "LOC110674777"
 [841] "LOC110674778" "LOC110674779" "LOC110674780" "LOC110674781" "LOC110674782" "LOC110674783" "LOC110674784" "LOC110674785" "LOC110674786" "LOC110674787" "LOC110674788" "LOC110674789"
 [853] "LOC110674790" "LOC110674791" "LOC110674792" "LOC110674793" "LOC110674794" "LOC110674795" "LOC110674796" "LOC110674797" "LOC110674798" "LOC110674799" "LOC110674800" "LOC110674801"
 [865] "LOC110674802" "LOC110674803" "LOC110674804" "LOC110674805" "LOC110674806" "LOC110674807" "LOC110674808" "LOC110674809" "LOC110674810" "LOC110674811" "LOC110674812" "LOC110674813"
 [877] "LOC110674814" "LOC110674815" "LOC110674816" "LOC110674817" "LOC110674818" "LOC110674819" "LOC110674820" "LOC110674821" "LOC110674822" "LOC110674823" "LOC110674824" "LOC110674825"
 [889] "LOC110674826" "LOC110674827" "LOC110674828" "LOC110674829" "LOC110674830" "LOC110674831" "LOC110674832" "LOC110674833" "LOC110674834" "LOC110674835" "LOC110674836" "LOC110674837"
 [901] "LOC110674838" "LOC110674839" "LOC110674840" "LOC110674841" "LOC110674842" "LOC110674843" "LOC110674844" "LOC110674845" "LOC110674846" "LOC110674847" "LOC110674848" "LOC110674849"
 [913] "LOC110674850" "LOC110674851" "LOC110674852" "LOC110674853" "LOC110674854" "LOC110674855" "LOC110674856" "LOC110674857" "LOC110674858" "LOC110674859" "LOC110674860" "LOC110674861"
 [925] "LOC110674862" "LOC110674863" "LOC110674864" "LOC110674865" "LOC110674866" "LOC110674867" "LOC110674868" "LOC110674869" "LOC110674870" "LOC110674871" "LOC110674872" "LOC110674873"
 [937] "LOC110674874" "LOC110674875" "LOC110674876" "LOC110674877" "LOC110674878" "LOC110674879" "LOC110674880" "LOC110674881" "LOC110674882" "LOC110674883" "LOC110675039" "LOC110675050"
 [949] "LOC110675094" "LOC110675095" "LOC110675096" "LOC110675097" "LOC110675098" "LOC110675099" "LOC110675100" "LOC110675101" "LOC110675102" "LOC110675103" "LOC110675104" "LOC110675105"
 [961] "LOC110675106" "LOC110675107" "LOC110675108" "LOC110675109" "LOC110675110" "LOC110675111" "LOC110675112" "LOC110675113" "LOC110675114" "LOC110675115" "LOC110675116" "LOC110675117"
 [973] "LOC110675118" "LOC110675119" "LOC110675120" "LOC110675121" "LOC110675122" "LOC110675123" "LOC110675124" "LOC110675125" "LOC110675126" "LOC110675127" "LOC110675128" "LOC110675129"
 [985] "LOC110675130" "LOC110675131" "LOC110675132" "LOC110675133" "LOC110675134" "LOC110675135" "LOC110675136" "LOC110675137" "LOC110675138" "LOC110675139" "LOC110675140" "LOC110675141"
 [997] "LOC110675142" "LOC110675143" "LOC110675144" "LOC110675145"
 [ reached getOption("max.print") -- omitted 18623 entries ]

AnnotationDbi::select(gff_ae_txdb,keys = keys_2_aaeg,columns=columns(gff_ae_txdb), keytype="GENEID")

'select()' returned 1:1 mapping between keys and columns

#AnnotationDbi::select(gff_ae_txdb,keys = keys_2_aaeg,columns=c("TXSTART","TXEND","TXNAME","CDSNAME","EXONNAME"), keytype="GENEID")


#retrieve all the transcripts from mitochondrial genome using this function as a granges
gr_aaeg_transcripts  <- transcripts(gff_ae_txdb)
gr_aaeg_genes  <- genes(gff_ae_txdb)

gr_aaeg_genes[1:3]

GRanges object with 3 ranges and 1 metadata column:
                 seqnames      ranges strand |     gene_id
                    <Rle>   <IRanges>  <Rle> | <character>
  CFI06_mgp01 NC_035159.1 11547-12488      - | CFI06_mgp01
  CFI06_mgp02 NC_035159.1 10323-11457      + | CFI06_mgp02
  CFI06_mgp03 NC_035159.1  9798-10319      + | CFI06_mgp03
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths

gr_aaeg_transcripts[1:3]

GRanges object with 3 ranges and 2 metadata columns:
         seqnames    ranges strand |     tx_id     tx_name
            <Rle> <IRanges>  <Rle> | <integer> <character>
  [1] NC_035159.1      1-69      + |     32449        <NA>
  [2] NC_035159.1   70-1095      + |     32450        <NA>
  [3] NC_035159.1 1096-1165      + |     32451        <NA>
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths

### Create list of granges to count overlaps of the samples for the different numts.
lists_of_granges_numts_chr_detail <- lapply(list_of_regions_ID,function(df) { 
  #grange_list <- list()
  #grange_list[[length(grange_list) + 1]] 
  df %>% dplyr::mutate(pos_chr_start_end = paste0("chrM:",start,"-",end,"-origin-",chrom)) %>%  dplyr::select(seqnames,start,end,strand,chrom,ID,pos_chr_start_end) %>% plyranges::as_granges()})
  #grange_list <-    
  #return(grange_list)


#Cerate a list of ranges to overlap to the genes in the CHR MT of Aedes aegypti
granges_list_aeag <- GRangesList(lists_of_granges_numts_chr_detail)





##find overlaps using plyranges (tutorial is really helpful)
gr_aaeg_genes <- sort(gr_aaeg_genes)
result_lists_aaeg <- list()
result_lists_aaeg_genes_hit <- list()
for (i in seq_along(1:length(granges_list_aeag))){
  print(i)
  result_lists_aaeg[[mcols(granges_list_aeag[[i]])[[2]][1]]] <- granges_list_aeag[[i]] %>%  join_overlap_inner(gr_aaeg_genes)
  result_lists_aaeg_genes_hit[[mcols(granges_list_aeag[[i]])[[2]][1]]] <- result_lists_aaeg[[i]] %>% as.tibble() %>%
    dplyr::group_by(gene_id) %>%
    dplyr::summarise(numts_hitting_X_time_the_gene=n())
  }

[1] 1

Warning: `as.tibble()` was deprecated in tibble 2.0.0.
ℹ Please use `as_tibble()` instead.
ℹ The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

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# result_lists_aaeg
# result_lists_aaeg_genes_hit


converter_g <- read.table(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Data/genes_dbxref.csv",sep = ",")
converter_t <- read.table(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Data/trnas_dbxref.csv",sep = ",")
converter_t <- converter_t[,c(2,1)]
names(converter_t) <- c("V1","V2")
converter_gt <- rbind(converter_g,converter_t)
names(converter_gt) <- c("symbol","dbxref")


### Convert the dbxref genen names to symbols
result_lists_aaeg_genes_hit_with_symbol <- lapply(result_lists_aaeg_genes_hit,function(nn) nn %>% dplyr::left_join(converter_gt,by = c("gene_id" = "dbxref")))


result_lists_aaeg_genes_hit_with_symbol_counts <- lapply(result_lists_aaeg_genes_hit_with_symbol,function(cc) cc %>% dplyr::group_by(symbol) %>% dplyr::summarize(count=dplyr::n(),col2_sum=sum(numts_hitting_X_time_the_gene)))

result_lists_aaeg_genes_hit_with_symbol_counts

$WGS_017_E20_F03

$WGS_021_E20_F02

$WGS_024_E20_F02

$WGS_025_E20_F02

$WGS_038_E20_F07

$WGS_040_E20_F02

$WGS_042_E20_F02

$WGS_043_E20_F02

$WGS_047_E20_F02

$WGS_050_E20_F02

$WGS_050_E20_F03

$WGS_060_E20_F06

$WGS_063_E20_F02

$WGS_080_E20_F02

$WGS_084_E20_F02

$WGS_088_E20_F05

$WGS_088_E20_F06

$WGS_095_E20_F11

$WGS_115_E20_F03

$WGS_132_E20_F10

$WGS_134_E20_F02

$WGS_211_E20_F02

$WGS_220_E20_F07

$WGS_230_E20_F06

$WGS_240_E20_F05

$WGS_246_E20_F05

$WGS_246_E20_F07

$WGS_249_E20_F02

$WGS_252_E20_F02

$WGS_253_E20_F01

$WGS_262_E20_F07

$WGS_266_E20_F04

$WGS_289_E20_F02

$WGS_299_E20_F09

$WGS_300_E20_F05

NA

mtDNA numts circos per sample

#mm="WGS_088_E20_F05"
# result_lists_aaeg_genes_hit_with_symbol_counts[[""]]

lapply(names(result_lists_aaeg_genes_hit_with_symbol_counts),function(mm) {
  print(paste0("plotting for sample", mm))
  
  #png(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/Visualization_samples/R_CIRCOS_RIL/",df[["V2"]][1],".png"),width = 680,height = 680)
  #svg(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/RIL_mtDNA_overlap_numts_",mm,"_circos.svg"),width = 9,height = 9)
  
  
  mt_genes_ae_2 <- mt_genes_ae
  mt_genes_ae_2$start <- mt_genes_ae_2$start/100000
  mt_genes_ae_2$end <- mt_genes_ae_2$end/100000


  numts_hits_df_merged_counted_start_end <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>%
    dplyr::right_join(result_lists_aaeg_genes_hit_with_symbol_counts[[mm]],by = c("gene"="symbol")) %>%
    dplyr::mutate(value=col2_sum) %>%
    dplyr::select(chr,start,end,value,gene) %>%
    dplyr::arrange(start,end)
  
  
  numts_hits_df_merged_counted_start_end$value_scaled <- numts_hits_df_merged_counted_start_end$value/10

  
  
  library(circlize)
  ref_fd_ae <- data.frame("Chromosome"=c("chrM"),"ChromStart"=c(1),"Chromend"=c(16790))
  #circos.genomicInitialize(ref_fd_ae)
  circos.genomicInitialize(ref_fd_ae,plotType = NULL)
  circos.genomicLabels(mt_genes_ae_2 ,
                       labels.column = 5,
                       cex=0.7,line_lwd=0.6, line_col="grey20",
                       connection_height = 0.019,
                       col=ifelse(mt_genes_ae_2$gene %in% numts_hits_df_merged_counted_start_end$gene[numts_hits_df_merged_counted_start_end$value_scaled > mean(numts_hits_df_merged_counted_start_end$value_scaled)],"red","black"),
                       labels_height = 0.2,niceFacing = TRUE,side = "outside")
  
  
  # circos.genomicLabels(mt_genes_2 ,labels.column = 5, cex=0.5,line_lwd=0.6, line_col="grey20", connection_height = 0.05, labels_height = 0.2,niceFacing = TRUE,side = "outside")

  circos.track(ylim=c(0, 1), panel.fun=function(x, y) {
  chr=CELL_META$sector.index
  xlim=CELL_META$xlim
  ylim=CELL_META$ylim#
  circos.text(mean(xlim), mean(ylim), chr, cex=0.8)}, bg.col = c("white"), bg.border=F, track.height=0.2) # the track height of the text specifies how thick is the track were we plot the bars
  
  circos.track(track.index = get.current.track.index(),
               track.height=0.8,
               ylim=c(0,1), #this track y lim specifies how big are the bars up to in the track y lim 
               panel.fun = function(x, y) {
                 circos.genomicAxis(h = "bottom", direction = "inside",labels.cex = 0.6,tickLabelsStartFromZero = FALSE)})
  # set_track_gap(gap = 0.05)
  
  
  # ddff <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>% dplyr::right_join(result_lists_aaeg_genes_hit_with_symbol_counts$WGS_211_E20_F02,by = c("gene"="symbol")) %>%dplyr::mutate(value=col2_sum) %>% dplyr::select(chr,start,end,value,gene) %>% dplyr::arrange(start,end)
# 
  # ddff_2 <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>% dplyr::right_join(result_lists_aaeg_genes_hit_with_symbol_counts$WGS_038_E20_F07,by = c("gene"="symbol")) %>% dplyr::mutate(value=col2_sum) %>% dplyr::select(chr,start,end,value,gene) %>% dplyr::arrange(start,end)


  library(RColorBrewer)
  green_pal <- colorRampPalette(brewer.pal(9, "Greens"))

 
  map_value_to_color <- function(value) {
    breaks <- seq(0,8,0.9)
    colors <- green_pal(9)
    colors[findInterval(value, breaks)]
  }
  
  circos.rect(xleft = numts_hits_df_merged_counted_start_end$start,
              ybottom = 0.01,
              xright = numts_hits_df_merged_counted_start_end$end,
              ytop = 0.02 + numts_hits_df_merged_counted_start_end$value_scaled,
              #col = rgb(0.4, 0.7, 0.4, alpha = 0.5),
              #col = ("#95D5B2"),
              col = map_value_to_color(numts_hits_df_merged_counted_start_end$value_scaled),
              #col = my_colors_scaled,
              border = "black")
  #circos.lines(CELL_META$cell.xlim, c(max(numts_hits_df_merged_counted_start_end$value_scaled)+0.02, max(numts_hits_df_merged_counted_start_end$value_scaled)+0.2),lwd = 2, lty = 2, col = "#A71246")
  #circos.lines(CELL_META$cell.xlim, c(min(numts_hits_df_merged_counted_start_end$value)+5, min(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "blue")
  circos.lines(CELL_META$cell.xlim, c(mean(numts_hits_df_merged_counted_start_end$value_scaled)+0.01, mean(numts_hits_df_merged_counted_start_end$value_scaled)+0.01),lwd = 2, lty = 2, col = "#03071E")
  
  
  
  #dev.off()
  #result_lists_aaeg_genes_hit_with_symbol_counts[[mm]]
})

[1] "plotting for sampleWGS_017_E20_F03"
[1] "plotting for sampleWGS_021_E20_F02"

[1] "plotting for sampleWGS_024_E20_F02"

[1] "plotting for sampleWGS_025_E20_F02"

[1] "plotting for sampleWGS_038_E20_F07"

[1] "plotting for sampleWGS_040_E20_F02"

[1] "plotting for sampleWGS_042_E20_F02"

[1] "plotting for sampleWGS_043_E20_F02"

[1] "plotting for sampleWGS_047_E20_F02"

[1] "plotting for sampleWGS_050_E20_F02"

[1] "plotting for sampleWGS_050_E20_F03"

[1] "plotting for sampleWGS_060_E20_F06"

[1] "plotting for sampleWGS_063_E20_F02"

[1] "plotting for sampleWGS_080_E20_F02"

[1] "plotting for sampleWGS_084_E20_F02"

[1] "plotting for sampleWGS_088_E20_F05"

[1] "plotting for sampleWGS_088_E20_F06"

[1] "plotting for sampleWGS_095_E20_F11"

[1] "plotting for sampleWGS_115_E20_F03"

[1] "plotting for sampleWGS_132_E20_F10"

[1] "plotting for sampleWGS_134_E20_F02"

[1] "plotting for sampleWGS_211_E20_F02"

[1] "plotting for sampleWGS_220_E20_F07"

[1] "plotting for sampleWGS_230_E20_F06"

[1] "plotting for sampleWGS_240_E20_F05"

[1] "plotting for sampleWGS_246_E20_F05"

[1] "plotting for sampleWGS_246_E20_F07"

[1] "plotting for sampleWGS_249_E20_F02"

[1] "plotting for sampleWGS_252_E20_F02"

[1] "plotting for sampleWGS_253_E20_F01"

[1] "plotting for sampleWGS_262_E20_F07"

[1] "plotting for sampleWGS_266_E20_F04"

[1] "plotting for sampleWGS_289_E20_F02"

[1] "plotting for sampleWGS_299_E20_F09"

[1] "plotting for sampleWGS_300_E20_F05"

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lapply(names(result_lists_aaeg_genes_hit_with_symbol_counts),function(mm) {
  print(paste0("saved sample", mm))
  write.table(x = result_lists_aaeg_genes_hit_with_symbol_counts[[mm]],
              file = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/RIL_mtDNA_overlap_numts_",mm,"_circos_table.csv"),sep = ",")})

[1] "saved sampleWGS_017_E20_F03"
[1] "saved sampleWGS_021_E20_F02"
[1] "saved sampleWGS_024_E20_F02"
[1] "saved sampleWGS_025_E20_F02"
[1] "saved sampleWGS_038_E20_F07"
[1] "saved sampleWGS_040_E20_F02"
[1] "saved sampleWGS_042_E20_F02"
[1] "saved sampleWGS_043_E20_F02"
[1] "saved sampleWGS_047_E20_F02"
[1] "saved sampleWGS_050_E20_F02"
[1] "saved sampleWGS_050_E20_F03"
[1] "saved sampleWGS_060_E20_F06"
[1] "saved sampleWGS_063_E20_F02"
[1] "saved sampleWGS_080_E20_F02"
[1] "saved sampleWGS_084_E20_F02"
[1] "saved sampleWGS_088_E20_F05"
[1] "saved sampleWGS_088_E20_F06"
[1] "saved sampleWGS_095_E20_F11"
[1] "saved sampleWGS_115_E20_F03"
[1] "saved sampleWGS_132_E20_F10"
[1] "saved sampleWGS_134_E20_F02"
[1] "saved sampleWGS_211_E20_F02"
[1] "saved sampleWGS_220_E20_F07"
[1] "saved sampleWGS_230_E20_F06"
[1] "saved sampleWGS_240_E20_F05"
[1] "saved sampleWGS_246_E20_F05"
[1] "saved sampleWGS_246_E20_F07"
[1] "saved sampleWGS_249_E20_F02"
[1] "saved sampleWGS_252_E20_F02"
[1] "saved sampleWGS_253_E20_F01"
[1] "saved sampleWGS_262_E20_F07"
[1] "saved sampleWGS_266_E20_F04"
[1] "saved sampleWGS_289_E20_F02"
[1] "saved sampleWGS_299_E20_F09"
[1] "saved sampleWGS_300_E20_F05"
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Count all samples together and circos it.

#Add circos

#this script run for all the samples summed up for all genes. could be also done individually.

#png(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/Visualization_samples/R_CIRCOS_RIL/",df[["V2"]][1],".png"),width = 680,height = 680)
svg(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/RIL_mtDNA_numts_all_samples_circos.svg"),width = 9,height = 9)


numts_hits_df_merged <- do.call(rbind,result_lists_aaeg_genes_hit_with_symbol_counts)
numts_hits_df_merged_counted <- numts_hits_df_merged %>% dplyr::group_by(symbol) %>% dplyr::summarize(count=dplyr::n(),total_all_samples_sum=sum(col2_sum))

mt_genes_ae_2 <- mt_genes_ae
  mt_genes_ae_2$start <- mt_genes_ae_2$start/100000
  mt_genes_ae_2$end <- mt_genes_ae_2$end/100000


numts_hits_df_merged_counted_start_end <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>% dplyr::right_join(numts_hits_df_merged_counted,by = c("gene"="symbol")) %>% dplyr::mutate(value=total_all_samples_sum) %>% dplyr::select(chr,start,end,value,gene) %>% dplyr::arrange(start,end)




library(circlize)
ref_fd_ae <- data.frame("Chromosome"=c("chrM"),"ChromStart"=c(1),"Chromend"=c(16790))

#circos.genomicInitialize(ref_fd_ae)
circos.genomicInitialize(ref_fd_ae,plotType = NULL)

mt_genes_ae_2 <- mt_genes_ae
mt_genes_ae_2$start <- mt_genes_ae_2$start/100000
mt_genes_ae_2$end <- mt_genes_ae_2$end/100000
circos.genomicLabels(mt_genes_ae_2 ,labels.column = 5, cex=0.7,line_lwd=0.6, line_col="grey20", connection_height = 0.019,col=ifelse(numts_hits_df_merged_counted_start_end$value > mean(numts_hits_df_merged_counted_start_end$value),"red","black"),
                     labels_height = 0.2,niceFacing = TRUE,side = "outside")
# circos.genomicLabels(mt_genes_2 ,labels.column = 5, cex=0.5,line_lwd=0.6, line_col="grey20", connection_height = 0.05, labels_height = 0.2,niceFacing = TRUE,side = "outside")



circos.track(ylim=c(0, 1), panel.fun=function(x, y) {
chr=CELL_META$sector.index
xlim=CELL_META$xlim
ylim=CELL_META$ylim#
circos.text(mean(xlim), mean(ylim), chr, cex=0.8)}, bg.col = c("white"), bg.border=F, track.height=0.2) # the track height of the text specifies how thick is the track were we plot the bars


circos.track(track.index = get.current.track.index(),
             track.height=0.8,
             ylim=c(0,120), #this track y lim specifies how big are the bars up to in the track y lim 
             panel.fun = function(x, y) {
               circos.genomicAxis(h = "bottom", direction = "inside",labels.cex = 0.6,tickLabelsStartFromZero = FALSE)})
# set_track_gap(gap = 0.05)



library(RColorBrewer)
green_pal <- colorRampPalette(brewer.pal(9, "Greens"))


map_value_to_color <- function(value) {
  breaks <- c(0, 10, 20, 30, 40, 50, 70, 90,110 )
  colors <- green_pal(9)
  colors[findInterval(value, breaks)] 
}



circos.rect(xleft = numts_hits_df_merged_counted_start_end$start,
            ybottom = 5,
            xright = numts_hits_df_merged_counted_start_end$end,
            ytop = 5 + numts_hits_df_merged_counted_start_end$value,
            #col = rgb(0.4, 0.7, 0.4, alpha = 0.5),
            #col = ("#95D5B2"),
            col = map_value_to_color(numts_hits_df_merged_counted_start_end$value),
            #col = my_colors_scaled,
            border = "black")
circos.lines(CELL_META$cell.xlim, c(max(numts_hits_df_merged_counted_start_end$value)+5, max(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "#A71246")
#circos.lines(CELL_META$cell.xlim, c(min(numts_hits_df_merged_counted_start_end$value)+5, min(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "blue")
circos.lines(CELL_META$cell.xlim, c(mean(numts_hits_df_merged_counted_start_end$value)+5, mean(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "#03071E")
   
dev.off()

null device 
          1 

write.table(x = numts_hits_df_merged_counted_start_end,file = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/RIL_mtDNA_numts_all_samples_circos.csv"),sep = ",")

Compare to already “reported NUMTS”

Plot the circos of all the genes from where the numts overlap of all the RIL samples vs the known sequences.

# numts_hits_df_merged_counted <- numts_hits_df_merged %>% dplyr::group_by(symbol) %>% dplyr::summarize(count=dplyr::n(),total_all_samples_sum=sum(col2_sum))

# mt_genes_ae_2 <- mt_genes_ae

# mt_genes_ae_2$start <- mt_genes_ae_2$start/100000
# mt_genes_ae_2$end <- mt_genes_ae_2$end/100000


# numts_hits_df_merged_counted_start_end <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>% dplyr::right_join(numts_hits_df_merged_counted,by = c("gene"="symbol")) %>% dplyr::mutate(value=total_all_samples_sum) %>% dplyr::select(chr,start,end,value,gene) %>% dplyr::arrange(start,end)

# svg(filename = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/knonwn_numts_circos_vs_RILs.svg",width = 9,height = 9)


library(circlize)
ref_fd_ae <- data.frame("Chromosome"=c("chrM"),"ChromStart"=c(1),"Chromend"=c(16790))

#circos.genomicInitialize(ref_fd_ae)
circos.genomicInitialize(ref_fd_ae,plotType = NULL)

mt_genes_ae_2 <- mt_genes_ae
mt_genes_ae_2$start <- mt_genes_ae_2$start/100000
mt_genes_ae_2$end <- mt_genes_ae_2$end/100000
circos.genomicLabels(mt_genes_ae_2 ,labels.column = 5, cex=0.7,line_lwd=0.6, line_col="grey20", connection_height = 0.019,col=ifelse(numts_hits_df_merged_counted_start_end$value > mean(numts_hits_df_merged_counted_start_end$value),"red","black"),
                     labels_height = 0.2,niceFacing = TRUE,side = "outside")

# circos.genomicLabels(mt_genes_2 ,labels.column = 5, cex=0.5,line_lwd=0.6, line_col="grey20", connection_height = 0.05, labels_height = 0.2,niceFacing = TRUE,side = "outside")



circos.track(ylim=c(0, 1), panel.fun=function(x, y) {
chr=CELL_META$sector.index
xlim=CELL_META$xlim
ylim=CELL_META$ylim#
circos.text(mean(xlim), mean(ylim), chr, cex=0.8)}, bg.col = c("white"), bg.border=F, track.height=0.2) # the track height of the text specifies how thick is the track were we plot the bars


circos.track(track.index = get.current.track.index(),
             track.height=0.8,
             ylim=c(0,120), #this track y lim specifies how big are the bars up to in the track y lim 
             panel.fun = function(x, y) {
               circos.genomicAxis(h = "bottom", direction = "inside",labels.cex = 0.6,tickLabelsStartFromZero = FALSE)})

# set_track_gap(gap = 0.05)



library(RColorBrewer)
green_pal <- colorRampPalette(brewer.pal(9, "Greens"))


map_value_to_color <- function(value) {
  breaks <- c(0, 10, 20, 30, 40, 50, 70, 90,110 )
  colors <- green_pal(9)
  colors[findInterval(value, breaks)] 
}



circos.rect(xleft = numts_hits_df_merged_counted_start_end$start,
            ybottom = 5,
            xright = numts_hits_df_merged_counted_start_end$end,
            ytop = 5 + numts_hits_df_merged_counted_start_end$value,
            #col = rgb(0.4, 0.7, 0.4, alpha = 0.5),
            #col = ("#95D5B2"),
            col = map_value_to_color(numts_hits_df_merged_counted_start_end$value),
            #col = my_colors_scaled,
            border = "black")
circos.lines(CELL_META$cell.xlim, c(max(numts_hits_df_merged_counted_start_end$value)+5, max(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "#A71246")

#circos.lines(CELL_META$cell.xlim, c(min(numts_hits_df_merged_counted_start_end$value)+5, min(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "blue")
circos.lines(CELL_META$cell.xlim, c(mean(numts_hits_df_merged_counted_start_end$value)+5, mean(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "#03071E")
   



### Add the known numts

results_kn_numts_genes_hit_with_symbol_counts_start_end <- mt_genes_ae_2 %>% dplyr::select(chr,start,end,gene) %>%
  dplyr::right_join(results_kn_numts_genes_hit_with_symbol_counts,by = c("gene"="symbol")) %>%
  dplyr::mutate(value=col2_sum) %>%
  dplyr::select(chr,start,end,value,gene) %>%
  dplyr::arrange(start,end)
  
results_kn_numts_genes_hit_with_symbol_counts_start_end$value_scaled <- results_kn_numts_genes_hit_with_symbol_counts_start_end$value/10


reds_pal <- colorRampPalette(brewer.pal(9, "Reds"))


map_value_to_color <- function(value) {
  breaks <- c(0, 10, 20, 30, 40, 50, 70, 90,110 )
  colors <- reds_pal(9)
  colors[findInterval(value, breaks)] 
}


circos.track(track.height=0.2,
             ylim=c(0,60))

circos.rect(xleft = results_kn_numts_genes_hit_with_symbol_counts_start_end$start,
            ybottom = 2.5,
            xright = results_kn_numts_genes_hit_with_symbol_counts_start_end$end,
            ytop = 2.5 + results_kn_numts_genes_hit_with_symbol_counts_start_end$value,
            #col = rgb(0.4, 0.7, 0.4, alpha = 0.5),
            #col = ("#95D5B2"),
            col = map_value_to_color(results_kn_numts_genes_hit_with_symbol_counts_start_end$value),
            #col = my_colors_scaled,
            border = "black")
circos.lines(CELL_META$cell.xlim, c(max(results_kn_numts_genes_hit_with_symbol_counts_start_end$value)+2.5,
                                    max(results_kn_numts_genes_hit_with_symbol_counts_start_end$value)+2.5),lwd = 2, lty = 2, col = "#A71246")

#circos.lines(CELL_META$cell.xlim, c(min(numts_hits_df_merged_counted_start_end$value)+5, min(numts_hits_df_merged_counted_start_end$value)+5),lwd = 2, lty = 2, col = "blue")
circos.lines(CELL_META$cell.xlim, c(mean(results_kn_numts_genes_hit_with_symbol_counts_start_end$value)+2.5,
                                    mean(results_kn_numts_genes_hit_with_symbol_counts_start_end$value)+2.5),lwd = 2, lty = 2, col = "#03071E")

   

###Here the last track of known numts are reported divided by 60 in the AREA to plot rather than 120 as in our numts.
### the added values are reduced to half in order to "Keep it proportional" 

# dev.off()


write.table(results_kn_numts_genes_hit_with_symbol_counts_start_end,file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/knonwn_numts_preprocess_3_table_circos_vs_RILs.csv",sep = ",")
#processed hits in the genes used for the knonwn or reported numts.
write.table(results_kn_numts_genes_hit_with_symbol,file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/knonwn_numts_overlapping_trnas_genes_preprocess_1_before_circos.csv.csv",sep = ",")

#processed hits in the genes used for the knonwn or reported numts, counted by gene or tRNA.
write.table(results_kn_numts_genes_hit_with_symbol_counts, file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Table/knonwn_numts_overlapping_trnas_genes_preprocess_2_before_circos.csv",sep = ",")

# Create sample data with one column "value" ranging from 0 to 100
data <- data.frame(value = 0:max(results_kn_numts_genes_hit_with_symbol_counts_start_end$value))

# Create a bar plot with gradient fill
# svg(filename = paste0("/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/Plots/known_numts_vs_RIL_circos_red_LEGEND.svg"),width = 3,height = 4 )
ggplot(data, aes(x = 1, y = value, fill = value)) + 
  geom_bar(stat = "identity", width = 1) +
  scale_fill_gradientn(colours = brewer.pal(9, "Reds"),guide = "legend") +
  coord_flip() +
  theme_void()

# dev.off()

save.image(file = "/home/botos/SVRAW1/botos/2023_02_08_NUMTS/final_numts/R/numts_R_process.RData",compress = "gzip")